Machine Learning

Facial recognition technology is now becoming known for everyone since it is being used to unlock mobile phones and laptops just by looking at your device. It is faster and convenient because you don’t have to remember and type your pin number each time when you want to unlock your phone.

The same technology can be used to simplify the registration and check-in process at events and expos. The prevailing process of event registration is completely manual or based on barcode scanning. Either the customer has to carry a print out of their registration confirmation or to carry a PDF copy on their phone.

Facial recognition can make the check-in process more convenient for the event attendees and organizers by transforming the event registration process. Here is how.

Faster Check-In

For large events with thousands of attendees, the major bottleneck will be at the registration/check-in counter. Since it is practically impossible to increase the number of registration stands beyond a limit, improving the speed of the check-in process is the best solution to avoid an unwanted rush.

Along with normal check-in counters, the event can have some facial recognition enabled check-in counters, where the attendee can get his badge printed within 10seconds just by looking at the camera.

This improves attendee satisfaction as they don’t have to carry a printout or scan through their messages or email to find the registration PDF.

Personalize Check-in Experience

It is possible to create a personalized experience at the check-in booth by recognizing the attendee’s face and welcoming him with a personalized greeting. This can create a wow-experience for the attendees.

This level of personalized experience is not possible in normal check-in booths, where the volunteer doesn’t know the attendee before they give the registration printout.

Talk to us to know how Logidots’ Machine Learning solutions can transform the check-in experience of your event.

Netflix, the world’s most popular subscription-based video streaming service, is known for using many Artificial Intelligence enabled services to provide better customer experience to their users. The most popular among these is it’s content recommendation engine that suggests movies and series to users.

In an academic paper by Netflix’s chief product officer, they mentioned that, Netflix saves around $1B every year through combined effect of personlisation and recommendation.

Netflix found that, a typical user who searches for content loses their interest after 60 – 90 seconds of searching. Here the problem is that, if the user is not able to find an interesting content within that time, the risk of abandoning the service is pretty high.

That’s where the role of recommendation engine is critical. It predicts contents that the user might be interested in, based on several factors and presents to the user in few seconds. This prevents the user from abandoning their service due to “non-availability” of interesting contents.

By calculating the number of users who might leave because of the above reason, Netflix estimates it to be around $1B or more every year. That’s why the AI based recommendation engine is worth a lot to the company.

AI and Machine learning continuous to be one of the most discussed technologies of all time. Unlike other fads that come and vanish, AI is definitely impacting human lives in many ways in the form of machine vision, smart predictions, autonomous cars etc

But, can Machine Learning accelerate the development of bespoke software and enhance the whole Software Development Lifecycle? AI can be applied to many areas in software development to make the process more efficient and faster. A 2016 Forrester Research survey, reveals that AI can even write code!

Here are some ways in which AI will improve bespoke software development.

Ideation and Planning

If you’ve been in the role of a Software Project Manager, you know how challenging that job is. Identifying product requirements properly from the stake holders, translating them to user stories and developer tasks, accomodating changes in requirement without breaking existing features.. The list goes on.

This is where Machine Learning can help. By analyzing data from several past projects, we might be able to build an automated system that translates requirements, or wireframes to actual user stories and developer tasks, and even assign them to right developers, which can reduce the project planning time by a huge extent.

Also, by analyzing previous data, a deep learning system can estimate tasks, where humans often make mistakes, and it can even predict risks and delays.

Design and Development

This is the major phase in SDLC life cycle, where human talent cannot be simply replaced, as it involves creativity and several other abilities which require human brains.

But still, we can see the growth in No-Code/Low-code platforms which significantly reduces the time in building applications.

Such platforms will continue to emerge and with Machine Learning, they’ll be more powerful enough to develop even enterprise applications without the need of a developer writing code from scratch.

However, we’re still years away from building such an AI system that can build a custom applications without human developers.

Software Testing

This is another area where AI is already making a huge impact. Manual Software Testing is time consuming, and with Agile methodologies, doing continuous testing is not always practical.

Machine learning can do code reviews by analyzing several thousands of opensource code bases available on the web. By pattern recognition, an AI system can predict and suggest improvement for a codeblock, and even auto-correct buggy code.

This will speed up the debugging process and human QA analysts can focus on testing the general usability (UI/UX) aspects of the software instead of trying to find and report functional / syntax errors.

Once AI completely automate test case preparation and testing process, the delivery time and quality of software can be significantly improved.

Even though we’re still far away from building fully automated software development systems, We can be sure that AI will play a massive role in the way how we develop software in the coming years.

How do you think AI will impact software development? Let us know in the comments!

Over the past few years, there is a steep increase in customer’s love towards self-service interactions. And this is why companies are investing their presence in every messenger platforms where their customers are present.

AI is disrupting every industry and insurance is no exception. Chatbots are one among the AI enabled tools that caught attention of enterprise CIOs because of the ROI it delivers. Chatbots with natural language processing capability helps in enhancing customer experience by providing an Onmi-channel experience to engage with customers across multiple platforms like Facebook, WhatsApp, WeChat etc.

Humans love conversations more than anything. Not convinced? Give a millennial a chat window and a support desk app and see what he prefers.

How it helps consumers

One of the advantages of using chatbots is that it will be available 24/7. You don’t have to restrict your support timing only during business hours. A chatbot can serve your customer with his queries even at midnight.

Also, you can deploy the same chatbot across multiple messenger platforms wherever works best for your target customers. Many of the platforms including Facebook Messenger, WhatsApp etc supports chatbot implementations.

Customers can get personalised product suggestions and answers based on their profile. For example, your chatbot will know how to deal with a millennial who works in Software Industry and what products he might be interested in.

What results are insurers getting from chatbots?

Chatbots can handle multiple conversations with different customers at the same time, which is not possible for a human. One common trait in support related conversations is that, majority of the customers are asking the same question. It’s like the 80/20 rule. A chatbot can efficiently handle this repeatedly asked queries so that your human support agents can spend time in answering more complex queries that requires true human intelligence.

A study reveals that, around 30% of the customer service calls can be answered by an intelligent chatbot and still maintaining high customer satisfaction.

Insurance companies experience a 30% drop in customer service costs as they deploy chatbot to assist their customers 24/7.

Claim processing is another are where chatbots can bring a huge impact.One of the major problems faced by insurance companies is fraudulent claims. Deploying a chatbot for processing claims can analyse past data, and predict fraudlent claims that a human might miss.

Integration with existing systems

A standalone chatbot is of no-use if it cannot integrate with your existing CRM and ERP systems. But, that’s not the case. Chatbots today can interact with your existing systems, and read/write data as required.

For example, when a chatbot services a customer, the chatbot can pull previous data from your CRM and personalise the conversation accordingly. And also, the chatbot can help you add more information to your CRM based on the data collected from the conversation with your customer.

Chatbots will continue to gain traction in the Insurance industry as more and more companies are adopting chatbots for customer support, sales and claim processing.

53% of service organizations expect to use chatbots within 18 months — a 136% growth rate that foreshadows a big role for the technology in the near future.

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